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Extending a recent suggestion to generate new instances for numericalblack-box optimization benchmarking by interpolating pairs of thewell-established BBOB functions from the COmparing COntinuous Optimizers (COCO)platform, we propose in this work a further generalization that allows multipleaffine combinations of the original instances and arbitrarily chosen locationsof the global optima. We demonstrate that the MA-BBOB generator can help fillthe instance space, while overall patterns in algorithm performance arepreserved. By combining the landscape features of the problems with theperformance data, we pose the question of whether these features are as usefulfor algorithm selection as previous studies suggested. MA-BBOB is built on thepublicly available IOHprofiler platform, which facilitates standardizedexperimentation routines, provides access to the interactive IOHanalyzer modulefor performance analysis and visualization, and enables comparisons with therich and growing data collection available for the (MA-)BBOB functions.

MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts